In the following discussion, certain articles and methods are described for background and introductory purposes. Nothing contained herein is to be construed as an “admission” of prior art. Applicant expressly reserves the right to demonstrate, where appropriate, that the articles and methods referenced herein do not constitute prior art under the applicable statutory provisions.
Comprehensive gene expression analysis and protein analysis have been useful tools in understanding mechanisms of biology. Use of these tools has allowed the identification of genes and proteins involved in development and in various diseases such as cancer and autoimmune disease. Conventional methods such as in situ hybridization and other multiplexed detection of different transcripts have revealed spatial patterns of gene expression and have helped shed light on the molecular basis of development and disease. Other technologies that have enabled the quantitative analysis of many RNA sequences per sample include microarrays (see Shi et al., Nature Biotechnology, 24(9):1151-61 (2006); and Slonim and Yanai, Plos Computational Biology, 5(10):e1000543 (2009)); serial analysis of gene expression (SAGE) (see Velculescu et al., Science, 270(5235):484-87 (1995)); high-throughput implementations of qPCR (see Spurgeon et al., Plos ONE, 3(2):e1662 (2008)); in situ PCR (see Nuovo, Genome Res., 4:151-67 (1995)); and RNA-Seq (see Mortazavi et al., Nature Methods, 5(7):621-8 (2008)). As useful as these methods are, however, they do not enable simultaneous measurement of the expression of many genes or the presence and/or activity of multiple proteins at many spatial locations in a sample.
Laser capture microdissection has permitted the analysis of many genes at a small number of locations, but it is very expensive, laborious, and does not scale well. Certain PCR assays in a 2D format preserve spatial information (see Armani et al., Lab on a Chip, 9(24):3526-34 (2009)), but these methods have low spatial resolution because they rely on physically transferring tissues into wells, which also prevents random access to tissue samples and high levels of multiplexing.
At present, there is a need to analyze at high resolution the spatial expression patterns of large numbers of genes, proteins, or other biologically active molecules simultaneously. There is also a need for reproducible, high-resolution spatial maps of biological molecules in tissues. The present disclosure addresses these needs.